Design Parameter Estimation using a Modified QFD Method to Improve Customer Perception
Bibliographic record
Abstract
This article presents an integrated approach to optimize cost while respecting the customer perception of a product using a modified Quality Function Deployment (QFD) method. This QFD method helps a design team to determine the effect of various design strategies for customer satisfaction. The new QFD method uses a two-phased approach for finding an optimum design strategy. During the first phase, the design team sets goals for customer perception for each customer attribute and relates them to those of its competitors (benchmarking); then, in the second phase, a goal-based model with a separated, mixed integer structure is used to minimize cost while respecting customer desires. The model defines fixed cost as a major improvement in design solutions such as changing parts, materials, or operational mechanisms. It also defines variable cost as a minor improvement in the current design solution. An illustrative example is given to demonstrate the use of the method, and a sensitivity analysis for budget limitation is shown. The method is applicable to a wide spectrum of design problems where, setting preferences over competitors’ products and respecting budget limitations are the major criteria in the design strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".